code: def _orig_features(graph, key):
      # repeat the feature to have length feature_dims
      if not isinstance(graph[key], torch.Tensor):
          graph[key] = torch.tensor(graph[key])
      if cfg.dataset.task_type == "regression" and "label" in key:
          graph[key] = graph[key].float()
      assert graph[key].ndim <= 2
      if graph[key].ndim == 1 and Graph._is_node_attribute(key):
          # n-by-1 tensor for node attributes
          graph[key] = graph[key].unsqueeze(-1)
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 5 for calling the FeatureAugment obj's underlying member method code with example usage: obj._orig_features(graph, key) without return types